Imaging biomarkers from AI federated learning

This MRFF project seeks to build a novel, hybrid AI learning ecosystem to generate clinically-relevant biomarkers of disease progression for the common, disabling neurological condition, multiple sclerosis (MS).  The MSBASE-XNAT imaging repository and I-MED clinical radiology site data will respectively form the key components of a unique central-federated AI learning environment, yielding algorithms that, validated in a clinical neurology environment, will set a benchmark in diagnostic MS imaging; track subclinical progression of the disease; direct therapeutic strategy; and mine hitherto untapped quantitative imaging data.

This project involves close collaboration with Prof Michael Barnett and Dr Chenyu Tim Wang from the Faculty of Medicine and Health.

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